A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins

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2016

From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins

Aida Verdes CUNY Graduate Center

Prachi Anand CUNY Hunter College

Juliette Gorson CUNY Graduate Center

Stephen Jannetti CUNY Hunter College

Patrick Kelly CUNY Hunter College

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This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Authors Aida Verdes, Prachi Anand, Juliette Gorson, Stephen Jannetti, Patrick Kelly, Abba Leffler, Danny Simpson, Girish Ramrattan, and Mandë Holford

This article is available at CUNY Academic Works: https://academicworks.cuny.edu/hc_pubs/336 toxins

Review From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins

Aida Verdes 1,2,3, Prachi Anand 1, Juliette Gorson 1,2,3, Stephen Jannetti 1,2, Patrick Kelly 1,2, Abba Lefﬂer 1,4, Danny Simpson 1,5, Girish Ramrattan 1 and Mandë Holford 1,2,3,*

1 Hunter College, The City University of New York, Belfer Research Building, 413 E. 69th Street, New York, NY 10021, USA; [email protected] (A.V.); [email protected] (P.A.); [email protected] (J.G.); [email protected] (S.J.); [email protected] (P.K.); [email protected] (A.L.); [email protected] (D.S.); [email protected] (G.R.) 2 The Graduate Center, City University of New York, 365 5th Ave, New York, NY 10016, USA 3 Sackler Institute for Comparative Genomics, Invertebrate Zoology, American Museum of Natural History, Central Park West & 79th St, New York, NY 10024, USA 4 Sackler Institute of Graduate Biomedical Sciences, New York University School of Medicine 550 1st Avenue, New York, NY 10016, USA 5 Tandon School of Engineering, New York University 6 MetroTech Center, Brooklyn, NY 11201, USA * Correspondence: [email protected]; Tel.: +1-212-896-0449

Academic Editor: Stephen Mackessy Received: 3 March 2016; Accepted: 7 April 2016; Published: 19 April 2016

Abstract: Animal venoms comprise a diversity of peptide toxins that manipulate molecular targets such as ion channels and receptors, making venom peptides attractive candidates for the development of therapeutics to benefit human health. However, identifying bioactive venom peptides remains a significant challenge. In this review we describe our particular venomics strategy for the discovery, characterization, and optimization of Terebridae venom peptides, teretoxins. Our strategy reflects the scientific path from mollusks to medicine in an integrative sequential approach with the following steps: (1) delimitation of venomous Terebridae lineages through taxonomic and phylogenetic analyses; (2) identification and classification of putative teretoxins through omics methodologies, including genomics, transcriptomics, and proteomics; (3) chemical and recombinant synthesis of promising peptide toxins; (4) structural characterization through experimental and computational methods; (5) determination of teretoxin bioactivity and molecular function through biological assays and computational modeling; (6) optimization of peptide toxin affinity and selectivity to molecular target; and (7) development of strategies for effective delivery of venom peptide therapeutics. While our research focuses on terebrids, the venomics approach outlined here can be applied to the discovery and characterization of peptide toxins from any venomous taxa.

Keywords: venomics; Terebridae; teretoxins; peptide toxins; animal venom; venom peptides; drug development; drug discovery; peptide therapeutics; drug delivery

1. Introduction Medicinal treatments have a storied history tied to natural products discovery and development. Natural products derived from plants and animals have been the source of traditional medicine for millennia, and more recently have become major sources of chemical diversity as drug leads, driving research efforts in pharmaceutical drug discovery and development [1,2]. The ascendancy of natural products was acknowledged with the awarding of the 2015 Nobel Prize in Physiology or

Toxins 2016, 8, 117; doi:10.3390/toxins8040117 www.mdpi.com/journal/toxins Toxins 2016, 8, 117 2 of 30

Medicine for the discovery of two revolutionary therapies based on natural compounds, Avermectin and Artemisinin. Avermectin has helped to nearly eradicate parasitic worm diseases such as river blindness and lymphatic filariasis, while Artemisinin represents the most effective treatment for malariaToxins known 2016, to8, 117 date [3]. The impact of these natural products on improving global human2 of 29 health is incalculable. Artemisinin. Avermectin has helped to nearly eradicate parasitic worm diseases such as river Theblindness journey and from lymphatic natural filariasis, product while discovery Artemisinin to therapy represents has the largely most effective focused treatment on small for chemical compoundsmalaria such known as Avermectinto date [3]. The and impact Artemisinin; of these natural however, products natural on improving peptides global are human increasingly health being investigatedis incalculable. as drug leads in pharmaceutical research [4]. In particular, peptides found in venomous organisms areThe a journey very promising from natural source product for discovery drug discovery. to therapy Successfulhas largely focused examples on small of drugs chemical developed from venomcompounds peptides such include as Avermectin Captopril and® Artemisinin;, based on ahowever, venom natural peptide peptides from the are Brazilian increasingly viper being and used investigated as drug leads in pharmaceutical research [4]. In particular, peptides found in venomous to treat hypertension [5,6]; exenatide (marketed as Byetta®), based on the Gila monster venom and used organisms are a very promising source for drug discovery. Successful examples of drugs developed ® as an anti-diabeticfrom venom peptides agent [7 include]; and ziconotideCaptopril®, based (Prialt on ),a basedvenom onpeptide a venom from the peptide Brazilian from viper the and predatory cone snailusedConus to treat magus hypertensionand used [5,6]; to exenatide treat chronic (marketed pain as [ 8Byetta,9]. Most®), based venom on the peptides Gila monster are disulfide-richvenom and varyand in used length as an from anti‐ 12–30diabetic residues agent [7]; inand cone ziconotide snails (Prialt to 40–80®), based residues on a venom in terebrids, peptide from scorpions, the and snakes [predatory10–12]. The cone relatively snail Conus small magus size and and used the stabilityto treat chronic provided pain by [8,9]. disulfide Most venom bridges peptides that characterize are disulfide‐rich and vary in length from 12–30 residues in cone snails to 40–80 residues in terebrids, natural peptides make them ideal candidates for drug leads. Venom peptides are predominantly being scorpions, and snakes [10–12]. The relatively small size and the stability provided by disulfide investigatedbridges for that the characterize development natural of drugpeptides therapies make them targeted ideal candidates to ion channels for drug and leads. receptors Venom [12–16]. Due to technologicalpeptides are predominantly constraints, being such investigated as size and for ease the development of collection, of drug venomous therapies organisms targeted to like ion snakes and scorpionschannels haveand receptors been traditionally [12–16]. Due to singled technological out forconstraints, drug discovery such as size research. and ease of However, collection, recent advancesvenomous in next-generation organisms like sequencing snakes and (NGS) scorpions techniques have been and traditionally improvements singled in proteomicout for drug methods have alloweddiscovery venom research. research However, to recent expand advances and include in next‐generation neglected sequencing venomous (NGS) invertebrates techniques and with great improvements in proteomic methods have allowed venom research to expand and include neglected potential, such as the conoideans (Figure1)[17–19]. venomous invertebrates with great potential, such as the conoideans (Figure 1) [17–19].

Figure 1. From mollusks to medicine. Overview of venomics approach for discovery, characterization, Figure 1. From mollusks to medicine. Overview of venomics approach for discovery, characterization, and development of therapeutics from Terebridae venom peptides. This strategy begins with a and developmentphylogenetic ofdelimitation therapeutics of venomous from Terebridae terebrid lineages venom to identify peptides. the species This that strategy are producing begins with a phylogeneticvenom delimitationto subdue their of prey venomous (shown in terebrid red); in lineagesyellow, identification to identify of the teretoxins species through that are omics producing venom(genomics, to subdue transcriptomics, their prey (shown proteomics); in red); in ingreen, yellow, synthesis identiﬁcation and structural of teretoxins characterization through of omics (genomics,teretoxins; transcriptomics, in blue, bioactivity proteomics); assays and inidentification green, synthesis of molecular and targets; structural and in characterizationpink, peptide of teretoxins;optimization in blue, and bioactivity development assays of delivery and identiﬁcation methods for potential of molecular terebrid therapeutics. targets; and in pink, peptide optimization and development of delivery methods for potential terebrid therapeutics.

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The Conoidea superfamily (cone snails, terebrids, and turrids s.l.) is an extremely diverse group of predatory marine neogastropods divided into 16 families, with several lineages characterized by having a venom apparatus used for predation [10,19,20]. The genus Conus, the most extensively studied among the conoideans, and from which the drug ziconotide (Prialt®) was discovered, includes species that produce very complex venoms with thousands of unique venom peptides, known as conotoxins or conopeptides [21–27]. As such, it is not surprising that conotoxins have been considerably studied for several decades. However, the ~700 described species of cone snails represent far less than half of the over 15,000 species that are estimated to comprise the Conoidea superfamily [28]. The family Terebridae, commonly known as auger snails, is an understudied lineage of conoideans that also has venomous representatives [29–33]. There are circa 400 described species of terebrids that live mostly in shallow sandy bottoms on tropical waters and have a characteristic elongated shell [33–35]. Terebrid venom peptides, referred to as teretoxins, are structurally similar to conotoxins, but due to the early divergence of terebrids and cone snails in the Paleocene [36], teretoxins represent highly divergent compounds with unique functionalities compared to conotoxins [10,19,37–39]. Despite their great potential, characterizing bioactive compounds in conoidean venom poses several challenges due mainly to their great species diversity, difficulty of sampling due to size and habitat, the small amounts of venom produced, and the scarcity of reference databases to identify novel venom peptides [40]. The most promising avenue to overcome these challenges is to apply interdisciplinary strategies that integrate molecular biology and biochemical analyses of venom compounds to optimize the characterization of peptides [41]. This strategy, often referred to as venomics, combines classic approaches for the study of biodiversity, such as taxonomy and phylogeny, with modern NGS techniques and proteomic methods, creating a robust evolutionary roadmap for effective drug discovery while greatly advancing knowledge on venom systematics and evolution [16,19,42–44]. In the present review, we describe our specific venomics approach to investigate Terebridae diversity and evolution, and to identify and characterize teretoxins and their potential for biomedical applications, paving the scientific route from mollusks to medicine (Figure1).

2. Phylogeny-Based Discovery of Teretoxins Traditionally molluscan species were chosen for venom research based on size, ease of collection, and quantity of venom produced. The lack of a methodological strategy led to the characterization of random venoms that sometimes corresponded to a mere single lineage [19]. As molecular biology, NGS, and proteomics technologies advanced, size and quantity of venom were no longer a restriction and it was possible to devise strategies that harnessed the evolutionary power of nature, investigating phylogenies and species relatedness to determine the most promising and diverse conoidean lineages to identify novel bioactive compounds through venomics analyses [22,45–47]. This venomics-based discovery strategy takes into account different characteristics, such as the presence of a venom apparatus, and demonstrates the importance of understanding phylogeny to enhance the identiﬁcation of venom peptides with potential pharmacological applications [19]. We follow this phylogeny-informed methodology to select appropriate terebrid study lineages and taxa (Figure2)[ 10]. Toxins 2016, 8, 117 4 of 30 Toxins 2016, 8, 117 4 of 29

Outgroup

Clade A

Clade F

Clade B

Clade C

Clade D

Clade E1

Clade E2

Clade E3

Clade E4

Clade E5

FigureFigure 2. Terebridae 2. Terebridae phylogeny. phylogeny. Cladogram Cladogram reconstructing reconstructing the the evolutionary evolutionary relationships relationships within within the the TerebridaeTerebridae family. family. Line Line color color indicates indicates presence presence or absence or absence of venom of venom apparatus. apparatus. Solid Solid red red lines lines indicate indicate clades in which all members have venom apparatus, dashed red lines indicate clades in which clades in which all members have venom apparatus, dashed red lines indicate clades in which only only some members have venom apparatus, and black lines indicate clades that lack venom some members have venom apparatus, and black lines indicate clades that lack venom apparatus. apparatus. Cladogram based on phylogenetic reconstruction from [48]. Cladogram based on phylogenetic reconstruction from [48]. 2.1. Terebridae Phylogenetics 2.1. Terebridae Phylogenetics Natural history and relatedness among species have been traditionally defined by morphology‐ Naturalbased phylogenetic history reconstructions. and relatedness This methodology among species is hampered have in been the Neogastropoda traditionally due defined to the by morphology-basedhigh levels of phylogenetichomoplasy and reconstructions. convergence in This morphological methodology characteristics is hampered [49–51]. in the Neogastropoda Thus, the due toadvantages the high levels of molecular of homoplasy phylogenetics, and convergence which inallows morphological for the comparison characteristics of thousands [49–51]. Thus, of the advantageshomologous of molecular characters phylogenetics, across species, which are of particular allows for interest the comparison among the ofTerebridae. thousands of homologous The first molecular phylogeny of the Terebridae was constructed based on analyses of a three‐ characters across species, are of particular interest among the Terebridae. gene matrix (12S, 16S, and COI) to define Terebridae lineages and their evolutionary history [52]. This Theinitial first Terebridae molecular phylogeny phylogeny confirmed of the the Terebridae monophyly was of the constructed group and baseddefined on five analyses distinct of a three-genelineages: matrix Acus (12S,(Clade 16S, B), andTerebra COI) (Clade to define C), Hastula Terebridae (Clade lineages D), Myurella and (Clade their evolutionary E), and a previously history [52]. This initialunidentified Terebridae fifth sister phylogeny clade that confirmed includes Pellifronia the monophyly jungi (Clade of the A) group [52]. Subsequent and defined molecular five distinct lineages:phylogeneticAcus (Clade analysis, B), Terebraincluding(Clade additional C), Hastula taxa from(Clade the D), EasternMyurella and (CladeWestern E), Pacific and a further previously unidentifiedresolved the fifth terebrid sister evolutionary clade that includes relationships,Pellifronia synonymizing jungi (Clade Acus clade A) [ B52 to]. Oxymeris Subsequent, recovering molecular phylogenetica previously analysis, unidentified including clade additional F that includes taxa fromthe Euterebra the Eastern and Duplicara and Western genera, Pacific and subdividing further resolved the terebridthe large evolutionary Myurella clade relationships, E into five lineages synonymizing (Clades E1–5)Acus clade[48] (Figure B to Oxymeris 2). The molecular, recovering phylogeny a previously unidentifiedof terebrids clade correlates F that includes with anatomical the Euterebra features,and Duplicaraspecificallygenera, the presence and subdividing or absence of the the large venomMyurella apparatus [53]. clade E into five lineages (Clades E1–5) [48] (Figure2). The molecular phylogeny of terebrids correlates with anatomical features, specifically the presence or absence of the venom apparatus [53]. It is important to mention that the accuracy of phylogenetic reconstructions is not guaranteed by any particular number of genes or taxa, even when bootstrap support values are high. In many cases, increasing gene number leads to higher support for the incorrect phylogenetic reconstruction; Toxins 2016, 8, 117 5 of 30 however, increasing taxon representation improves the accuracy, providing a phylogeny that is more likely to represent the evolutionary history of the group. Therefore, the accuracy of phylogenetic estimations as well as the accuracy of inferences about evolutionary processes based on phylogenies can be significantly improved by extensive and thorough taxon sampling efforts [54,55]. This was evident in the last Terebridae phylogeny published in 2012, which expanded the taxon sampling from the Western Pacific region to include species from the Eastern Pacific as well. The expansion of taxon sampling allowed us to substantially refine the relationships of the Myurella clade lineages and to recover a previously unidentified clade F (Figure2)[ 48,52]. For this reason, we are constantly working on increasing taxon sampling to improve the phylogenetic reconstruction of the Terebridae and currently have samples of ~150 species, which represents ~38% of the 400 currently known terebrid taxa. Another source of conflict when inferring phylogenies is determining the root of the tree. The root of a tree represents its deepest split and determines the direction of all subsequent evolutionary events. An incorrect root can result in erroneous inferences of species relationships and character evolution and, therefore, determining the root accurately is critical for phylogenetic analysis. One of the most common methods applied to root phylogenetic trees is the use of an outgroup that represents the most closely related taxa or sister group. Unfortunately, it is not always certain what the closest relatives to a particular group are and, even when this is known, sometimes the closest relatives are rather distantly related [56,57]. Luckily, the Conoidean phylogeny has been thoroughly studied and there is extensive evidence that cone snails and turrids are the most closely related taxa to Terebridae [20,58]. Therefore, the Terebridae phylogenetic reconstructions are rooted with representative species of Conidae and Turridae as closely related outgroups and species from the neogastropod family Harpidae as distant outgroups [52], providing a robust and accurate root for phylogenetic inference.

2.2. Venom Apparatus Evolution The venom apparatus as defined in the Conidae consists of a venom bulb, a venom gland, a radular sac, and a proboscis. However, the Terebridae have been traditionally described as having three distinct foregut anatomies: (I) salivary glands present, but lack of a radular sac and venom apparatus; (II) identical to a Conus venom apparatus with a radula delivery system and venom gland for venom production; and (III) lack of a venom apparatus, but presence of an accessory proboscis structure [59,60]. These early anatomical descriptions have been revised in recent publications and expanded to include additional important features of terebrid anatomy, such as marginal radular teeth [61–63]. Terebrids display the widest diversity of marginal radular teeth types in all conoideans including duplex, solid recurved, flat, semi-enrolled, and hypodermic. These teeth are absent in the lineages in which the venom apparatus has been lost [48]. Our recent efforts have also revealed that the evolution of the Terebridae foregut anatomy is rather complex and certain features have originated independently across the phylogeny, while others including the proboscis, radula, and venom gland have been lost in several lineages [48]. The venom gland specifically, was lost eight times throughout Terebridae evolution in clades F, B, and E1, and in certain members of E2, E3, E4, and E5 [48]. This level of gain and loss of venom-related characters is similar to what has been observed in other venomous taxa such as fish, lizards, and snakes [64–66]. The morphological diversity of foregut anatomies in the Terebridae is hypothesized to correlate with the varying diet and feeding strategies among the different terebrid lineages, and it has been suggested as one of the main drivers of species diversification in the group. Moreover, terebrids with the Type II feeding apparatus feed on their prey in a manner that mirrors that of cone snails. Specifically, Hastula and Terebra species use a hypodermic radular tooth at the end of the proboscis to envenomate their vermivorous prey [32,34,48,59,67,68]. Venom variability in cone snails has been extensively studied and the differences in peptide diversity and expression patterns among different species have been attributed to divergent diets and defensive pressures, which in turn drive species diversification [69–71]. Consequently, we can expect a similar correlation pattern to that of cone Toxins 2016, 8, 117 6 of 30 snails, with increased species numbers in the Terebridae lineages that have venom apparatus, and, accordingly, a vast diversity of terebrid venom peptides. As the venom apparatus is not found in all terebrid lineages, the first step to characterize teretoxins is to successfully identify the lineages that have a venom gland and are actively expressing venom peptides to subdue their prey or for defensive purposes. The molecular phylogeny and characterization of terebrid foregut anatomy completed to date provides a roadmap for efficiently identifying the most promising terebrid lineages for venomics investigation (Figure2). Understanding the relationships between terebrid lineages aids in effectively identifying divergent terebrid groups for the discovery of novel peptides with diverse molecular activities that can be used to further drug discovery research.

3. Teretoxin Identification and Classification The traditional approach for peptide toxin discovery employed biochemical techniques such as venom fractionation by Liquid Chromatography (LC), Edman Degradation to determine primary amino acid sequences, and Mass Spectrometry (MS) to characterize crude venom extracts. However, with the decreasing costs and increasing efficiency of NGS techniques and improvements in high-throughput proteomic methods, the venomics landscape is rapidly changing and currently even organisms that produce exceptionally small quantities of venom can be characterized [72]. Transcriptomic studies of venom duct and venom gland tissue are rapidly growing for a number of venomous taxa, providing large amounts of data that allow the analysis of expressed gene products and the identification of a great number of putative peptide toxins [73–77]. However, these studies also have disadvantages. For example, venom peptides identified by genomic methods cannot be validated without proteomic evidence [78–83]. Conveniently, modern technologies allow the use of sequence databases generated from genomic data or available in public databases such as Conoserver and Tox-Prot to aid in the identification of peptides from proteomic data [84–87]. Additionally, the large number of putative venom peptides and proteins identified by NGS and high-throughput proteomics can be classified into gene superfamilies using phylogenetic methodologies to facilitate their interpretation and assist with functional predictions [10,25].

3.1. Venom Gland Transcriptomics Venom gland transcriptome studies have proven very useful to characterize putative venom compounds in small invertebrates such as the Terebridae. We have taken advantage of these methods and recently published a comparative analysis of the venom gland transcriptomes of two Terebridae species, providing important insights into terebrid venom composition and evolution [10]. In this work we developed an in silico bioinformatics pipeline that can be broadly applied to investigate transcriptomic data from other venomous organisms (Figure3). The pipeline begins offline with collection of species and tissue dissections. Specifically, terebrid specimens are collected from tropical marine habitats and dissected to extract the venom gland, which is flash frozen in liquid nitrogen or fixed in RNAlater and stored at ´80 ˝C until ready for use. For our purposes, total RNA is extracted from venom gland tissue with the Qiagen RNeasy Micro Kit following the manufacturer protocol and sequenced using Illumina HiSeq 2500 with v. 4 technology using a paired end flow cell and 100 ˆ 2 cycle sequencing. The quality of the raw Illumina sequence reads is then evaluated with FastQC (http://www. bioinformatics.babraham.ac.uk/projects/fastqc). FastQC generates a profile of sequencing data, including graphs of quality per base, GC-content, k-mer content, and sequence length distributions among others, allowing for a quick assessment of potential sequencing errors [88]. Trimmomatic [89] is subsequently used to trim poor quality reads and to remove any Illumina adapters present and the processed reads are assembled de novo using Trinity [90,91]. Using Trimmomatic to remove low-quality reads can lead to a higher quality assembly, but the assembly itself and all putative venom peptides identified must be treated with caution due to the lack of a reference terebrid genome and ToxinsToxins 20162016, ,88, ,117 117 77 of of 29 30 reference terebrid genome and the complexity of assembling hypervariable venom peptides, which canthe be complexity a challenge of for assembling existing assembly hypervariable software venom programs. peptides, which can be a challenge for existing assembly software programs.

FigureFigure 3. 3. BioinformaticsBioinformatics pipeline pipeline for for terebrid terebrid transcriptome transcriptome analyses. analyses. Summary Summary of of bioinformatics bioinformatics pipelinepipeline for for the identiﬁcationidentification of of putative putative teretoxins teretoxins from from RNA-Seq RNA‐Seq data. data. Colors Colors indicate indicate different different stages stagesof the of process. the process. Orange Orange indicates indicates RNA extractionRNA extraction and sequencing, and sequencing, yellow yellow indicates indicates raw read raw quality read qualityﬁltering, filtering, red indicates red indicates transcriptome transcriptome assembly, assembly, green indicates green indicates ORF and ORF signal and sequence signal predictionsequence predictionfrom transcripts, from transcripts, and purple and indicates purple transcript indicates transcript annotation. annotation.

AfterAfter transcriptome transcriptome assemblyassembly is is completed, completed, TransDecoder TransDecoder is used is used to predict to predict coding coding regions regions within withinthe transcripts. the transcripts. A sequence A sequence is classified is classified as a candidate as a candidate protein-coding protein region‐coding based region on nucleotidebased on nucleotidecomposition, composition, open reading open framereading (ORF) frame length, (ORF) length, and optionally, and optionally, a match a match to a Pfamto a Pfam domain domain [90]. [90].As venom As venom is mainly is mainly composed composed of of secreted secreted proteins proteins and and peptides, peptides, SignalP SignalP isis then used to to predict predict signalsignal peptide peptide sequences sequences in in these these putative putative protein protein-coding‐coding regions regions [92]. [92]. Using Using a a custom custom Perl Perl script, script, all all transcriptstranscripts surviving surviving these these two two initial initial filters filters are are then then searched searched against against an an in in-house‐house venom venom database database usingusing the the BLASTp BLASTp tool tool [93]. [93]. This This database database includes includes all all known known venom venom proteins proteins and and peptides peptides available available inin public public databases databases such such as asConoserver Conoserver and and Tox Tox-Prot‐Prot along along with with putative putative teretoxins teretoxins identified identified by our by groupour group [85–87]. [85 –All87 ].transcripts All transcripts with hits with to hits a protein to a protein in the database in the database with an with e‐value an e-value of 1e–5 of or 1e–5 better or andbetter sequence and sequence similarity similarity of at least of at least40% 40%are then are then searched searched against against the the NCBI NCBI non non-redundant‐redundant (nr) (nr) databasedatabase using using the the BLASTx BLASTx tool tool with with the the same same e e-value‐value and and sequence sequence similarity similarity thresholds. thresholds. The The results results fromfrom the the two two BLAST BLAST searches searches are are compared, compared, and and those those with with a a better better hit hit to to a a protein protein in in the the venom venom databasedatabase are are considered considered putative putative teretoxins teretoxins and and further further investigated. investigated. The The high high variation variation present present in in venomsvenoms makes makes identification identification via homology comparisoncomparison potentiallypotentially error-prone, error‐prone, with with a a high high number number of offalse false positive positive predictions. predictions. Without Without verification verification via experimental via experimental techniques techniques such as mass such spectrometry, as mass spectrometry,the actual existence the actual of predicted existence teretoxins of predicted from teretoxins our pipeline from cannot our pipeline be determined cannot with be determined certainty. with certainty.

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The amino acid sequences of putative teretoxins are also processed for mapping, annotation, and, specifically, the assignment of Gene Ontology (GO) terms in BLAST2GO [94,95]. The assignment of GO terms provides information about putative gene or protein domain functions, strengthening the identification of candidate teretoxins. BLAST2GO is also used to identify potential venom peptides when transcripts that encode a signal sequence show no sequence homology to proteins in the venom database through BLAST searches. In this case, protein family IDs and specific protein domains are identified through an automated model-based approach based on InterProScan [96,97]. Following this approach, a carefully curated and annotated final list of candidate teretoxins is generated, allowing the classification of transcripts into functional categories for comparative studies across taxa.

3.2. Identification of Teretoxin Superfamilies Conoidean venom peptides have a characteristic structure, namely, a signal peptide sequence followed by a propeptide region and a terminal cysteine-rich mature peptide. Conotoxins have been classified into “gene superfamilies” according to the percentage of sequence identity of their signal peptide [98]. Venom gene superfamilies are hypothesized to reflect the evolutionary history of the conotoxin multigenic system. Puillandre et al. [99] recently validated this hypothesis and provided a phylogenetic framework for the classification of novel conotoxins. With the increasing number of putative conotoxins currently identified though transcriptome sequencing, the phylogenetic classification of conotoxins into venom gene superfamilies facilitates their interpretation and aids in predicting their biological function [24,25]. Similar to conotoxins, teretoxins are expressed as a single gene product with a signal sequence, propeptide region, and a cysteine-rich mature peptide on the C-terminal. While teretoxin gene sequences have been previously reported, there have been no teretoxin gene superfamilies described due mainly to lack of available data [37–39]. To address this gap, we recently proposed the first classification of Terebridae teretoxin gene superfamilies, providing a phylogenetic framework for the classification of novel terebrid peptides [10]. Escalating the previous definition used to describe a conotoxin superfamily, we define a teretoxin superfamily using three criteria: (i) independent lineage with high support values (bootstrap ě70 and posterior probability ě90); (ii) sequence identity within the superfamily to be greater than or equal to 60%; and (iii) the pattern of cysteines is different than in the sister clade. Through comparative analyses of the venom gland transcriptomes of Terebra subulata and Triplostephanus anilis, 139 novel putative teretoxins were identified, and following a phylogenetic approach 14 putative terebrid toxin gene superfamilies were described, 13 of which are unique to the Terebridae and thus distinct from any currently known conotoxin superfamilies (Figure4). The significant differences in the venom profiles of cone snails and terebrids support the premise that the early divergence of the two neogastropod lineages led to distinct venom cocktails [36]. These results illustrate the power of NGS techniques to provide data that can greatly expand venom evolutionary research. Toxins 2016, 8, 117 9 of 30 Toxins 2016, 8, 117 9 of 29

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Figure 4.FigureTeretoxin 4. Teretoxin gene gene superfamilies. superfamilies. Phylogenetic Phylogenetic reconstruction of teretoxin of teretoxin gene genesuperfamilies superfamilies adapted from [10]. Clades representing teretoxin superfamilies are indicated in blue. The cysteine adapted from [10]. Clades representing teretoxin superfamilies are indicated in blue. The cysteine framework that characterizes each superfamily is denoted in purple and the corresponding cysteine frameworkscaffold that in green. characterizes Terebrid superfamily each superfamily TM is the only is denoted one with inknown purple homology and to the a conotoxin corresponding cysteinesuperfamily. scaffold in green. Terebrid superfamily TM is the only one with known homology to a conotoxin superfamily. 3.3. Venom Proteomics and Proteogenomics Analyses 3.3. Venom ProteomicsAs most NGS and bioinformatics Proteogenomics pipelines, Analyses the one outlined here to analyze terebrid venom gland RNA‐Seq data, is heavily reliant on sequence homology searches, thus hindering the ability to Asidentify most NGS novel bioinformatics peptide toxins and pipelines, venom proteins the one [43]. outlined While there here are to some analyze computational terebrid methods venom gland RNA-Seqsuch data, as InterProScan is heavily reliant [96,97] on that sequence can aid in homology the identification searches, of putative thus hindering venom peptides the ability without to identify novel peptidesequence toxins homology and venomto known proteins peptide toxins, [43]. While the presence there areof the some predicted computational mature peptides methods in the such as InterProScanvenom [ cannot96,97] be that confirmed can aid without in the proteomic identification evidence of putative[43]. venom peptides without sequence The best method to date for the characterization of novel venom peptides is through MS homology to known peptide toxins, the presence of the predicted mature peptides in the venom cannot proteomic analyses of venom extracts. Notably, the technology and methodology employed to be confirmedidentify without and validate proteomic venom peptides evidence via [ 43proteomics]. analyses has vastly changed in recent years. In Thetraditional best method bottom‐ toup dateproteomics, for the enzymatic characterization digestions of of venom novel samples, venom liquid peptides chromatography is through MS proteomic(LC), analyses and tandem of mass venom spectrometry extracts. (MS/MS) Notably, analyses the technology were used to andidentify methodology venom peptides employed in a to identifysample. and validate The bottom venom‐up approach peptides can via be useful, proteomics but due analysesto loss of peptides has vastly during changed purification in recent it can years. In traditionalprove unsuccessful bottom-up proteomics,at identifying enzymaticcomplete sequences, digestions especially of venom when samples, looking for liquid novel chromatography peptides. In top‐down proteomics, individual intact venom proteins can be characterized and profiled using a (LC), and tandem mass spectrometry (MS/MS) analyses were used to identify venom peptides in direct analysis that compares statistically meaningful numbers in the sample to determine relative a sample.expression The bottom-up levels of intact approach peptides can [72,79,100]. be useful, While but the due debate to loss over of top peptides‐down versus during bottom purification‐up it can proveproteomics unsuccessful continues, attop identifying‐down has completeseveral attractive sequences, features especially for de novo when venom looking peptide for novel peptides.identification In top-down [101]. proteomics, The top‐down individual approach intact involves venom the analysis proteins of intact can be proteins characterized typically andusing profiled using aelectrospray direct analysis ionization that and compares high‐resolution statistically mass analysis meaningful and is numbersbeing increasingly in the sampleused to analyze to determine relative expression levels of intact peptides [72,79,100]. While the debate over top-down versus bottom-up proteomics continues, top-down has several attractive features for de novo venom peptide identification [101]. The top-down approach involves the analysis of intact proteins typically using electrospray ionization and high-resolution mass analysis and is being increasingly used to analyze single proteins or simple protein mixtures, including recent proteovenomic analyses of peptidic and Toxins 2016, 8, 117 10 of 30 small-protein venoms [72,79,102]. For example, Quinton et al. [100] were successful in introducing a rapid top-down sequencing method that used MALDI matrix enhancing in-source decay (ISD) to identify disulfide-bridged peptides in Conus venoms. This approach has not only improved the analysis and characterization of animal venoms, but it has also further enabled the identification of post-translational modifications (PTMs) [43,103]. PTMs are very common in conotoxins and can impact their specificity and activity [104–106]. The presence of PTMs cannot be reliably inferred from sequence data alone and must be confirmed by MS analysis of the pure native venom extract [47]. For example, proteomic analysis of the venom gland of Conus textile identified 31 conotoxins and 25 PTMs, while the venom gland transcriptome analysis of Conus tribblei revealed 136 putative conotoxins, and no PTMs [78,107]. While the number of putative conotoxins identified through transcriptomic analyses is much greater, without proteomic evidence none of the 136 Conus tribblei putative conotoxins can be validated, nor any potential PTMs identified. Consequently, a combined proteomics–genomics approach, or proteogenomics, represents the most comprehensive and promising method for the discovery of novel toxins and the characterization of animal venoms in general, and Terebridae venom in particular [41,43,84]. With this approach, species-specific protein sequence databases generated from genomic and transcriptomic data are used to identify novel peptides, not present in reference databases, from proteomic data. In addition, proteomic data provides evidence of gene expression, validating the gene models predicted from genomic and transcriptomic data. The venom peptides, validated through proteogenomics methods, can then be synthesized and characterized to investigate their function and molecular targets.

4. Chemical and Recombinant Peptide Synthesis of Teretoxins Teretoxins are a valuable reservoir of bioactive compounds; however, due to the scant quantities of venom produced by terebrids, it is difficult to obtain sufficient amounts of venom peptides for appropriate biochemical characterization. This obstacle can be overcome by producing synthetic versions of the peptides found in venom extracts. The three most common ways to obtain venom peptides synthetically are liquid-phase peptide synthesis (LPPS), solid-phase peptide synthesis (SPPS), and recombinant biology techniques [72,108,109]. Each method has advantages and disadvantages, such as the inexpensiveness and simplicity of LPPS that comes at the cost of yield and time. SPPS in turn, offers rapid syntheses, but depending on the peptide, obtaining the native cysteine fold can be problematic [110]. Recombinant synthesis allows for high yield and purity, but does not easily permit the incorporation of unnatural amino acids or site-specific labeling of peptides. The typically small volume of venom produced by terebrids requires multiple synthetic approaches including both chemical and recombinant synthesis methods [72,111].

4.1. Solid Phase Peptide Synthesis SPPS was first developed by Robert Bruce Merrifield in the second half of the twentieth century and has become a standard synthesis method for both peptides and proteins [112]. Through SPPS venom peptides can be rapidly synthesized, allowing the incorporation of unnatural amino acids and peptide backbone modification. The SPPS initiates on the carboxylic end of the last amino acid in a peptide sequence, which is bound to an insoluble solid support or resin. In this technique, a three-step deprotection, activation, and coupling process is repeated until the peptide of interest is completed, at which point it is removed by cleavage from the solid support resin (Figure5A). The insoluble nature of the resin allows excess reagent to be used to drive the amino acid coupling reaction to completion, and then all excess is washed away at each step in preparation for the next reaction. In the first SPPS iteration, the amino-terminus of each amino acid is protected from unwanted reaction by an acid labile tert-Butyloxycarbonyl (BOC) group. In the past few decades several solid support resins have been developed, as well as the now widely used base-labile fluorenylmethyloxycarbonyl (FMOC) amino-terminus protecting group for amino acids [113,114]. The FMOC protecting group is removed or deprotected with a strong base and the next amino acid is activated and then added to the growing Toxins 2016, 8, 117 11 of 30 Toxins 2016, 8, 117 11 of 29 peptideis activated chain and (Figure then5 A).added Activation to the growing facilitates peptide the coupling chain reaction (Figure and5A). a Activation peptide bond facilitates is formed the betweencoupling the reaction amino and acid a residuespeptide bond (Figure is 5formedA). between the amino acid residues (Figure 5A). VenomVenom peptides, peptides, which which are are typically typically rich inrich cysteines, in cysteines, pose several pose challengesseveral challenges for SPPS. for However, SPPS. mostHowever, of these most challenges of these challenges can be overcome can be overcome by using by a using copolymer a copolymer solid support solid support that contains that contains both polystyreneboth polystyrene and polyethylene and polyethylene glycol. glycol. The polystyrene The polystyrene and polyethylene and polyethylene glycol copolymer glycol copolymer has greater has stabilitygreater stability in acidic in environments, acidic environments, higher swelling, higher swelling, and prevention and prevention of racemization, of racemization, which is which a concern is a forconcern any peptide for any sequence peptide withsequence multiple with cysteines multiple or cysteines histidines or [ 115histidines]. Another [115]. strategy Another to successfully strategy to synthesizesuccessfully disulfide synthesize rich disulfide peptides rich is to peptides increase purityis to increase and yield purity by incorporating and yield by pseudoproline incorporating dipeptidespseudoproline to reduce dipeptidesβ-sheet to reduce formation β‐sheet during formation synthesis during [116 ].synthesis Typically, [116]. cysteine Typically, residues cysteine are orthogonallyresidues are orthogonally protected using protected an acetamidomethyl using an acetamidomethyl group on select group cysteines, on select and cysteines, trityl groups and on trityl the remaininggroups on cysteinesthe remaining to allow cysteines for site-specific to allow deprotection for site‐specific [117 ].deprotection More recently [117]. substituting More recently select cysteinessubstituting for selenocysteinesselect cysteines [ 113for ,118selenocysteines] significantly [113,118] advanced significantly the synthesis advanced and folding the of synthesis cysteine richand venomfolding peptides. of cysteine SPPS rich has venom been thepeptides. method SPPS of choice has been for the the synthesis method ofof severalchoice conotoxinsfor the synthesis and also of forseveral incorporation conotoxins of unnaturaland also for amino incorporation acids such asof Dunnatural-amino acids amino [43 ,64acids,110 –such113]. as We D have‐amino recently acids applied[43,64,110–113]. SPPS to We successfully have recently synthesize applied Tv1, SPPS a 23-amino to successfully acid teretoxin synthesize from Tv1,Terebra a 23 variegata‐amino acid[72] (Figureteretoxin5B,C). from Terebra variegata [72] (Figure 5B,C).

FigureFigure 5.5. ChemicalChemical synthesissynthesis ofof teretoxinteretoxin Tv1.Tv1. ((aa)) AutomatedAutomated cyclecycle ofof solid-phasesolid‐phase peptidepeptide synthesissynthesis usingusing FMOCFMOC chemistry;chemistry; (b)) RP-HPLCRP‐HPLC chromatogramchromatogram of Tv1 synthesissynthesis (linear) and folding reaction. TheThe foldedfolded conformationconformation isis indicatedindicated byby thethe pinkpink diamonddiamond andand thethe linearlinear conformationconformation byby thethe yellow diamond.diamond. ((cc)) NMRNMR structurestructure ofof chemicallychemically synthesizedsynthesized Tv1.Tv1. DisulﬁdeDisulfide bondsbonds areare depicteddepicted inin yellow.yellow.

4.2.4.2. RecombinantRecombinant SynthesisSynthesis RecombinantRecombinant expression techniques techniques are are a a great great alternative alternative for for the the synthesis synthesis of peptides of peptides that that are areproblematic problematic for SPPS for SPPS due to due length to length or complexity, or complexity, such as such many as teretoxins, many teretoxins, which can which have can a length have aof length up to 70 of amino up to 70acids. amino Recombinant acids. Recombinant expression expressionin Escherichia in coliEscherichia is a well coli‐establishedis a well-established and popular andmethod popular for the method production for the of production recombinant of recombinantproteins in which proteins the ingene which of interest the gene is ofcloned interest in an is clonedexpression in an vector, expression transformed vector, transformed into the host, into and the induced, host, and providing induced, providinga protein product a protein ready product for readypurification for purification [119]. There [119]. have There been have beenseveral several examples examples published published in inthe the literature literature describingdescribing methodologiesmethodologies forfor recombinantrecombinant expressionexpression ofof disulfide-richdisulfide‐rich peptidespeptides [[108,120–130].108,120–130]. TheseThese studiesstudies highlighthighlight important important aspects aspects that that must must be considered be considered for recombinant for recombinant expression expression of peptides, of includingpeptides, theincluding choice the of achoice fusion of tag, a fusion purification tag, purification method, host method, species host and species strain, and and strain, cleavage and technique. cleavage Fortechnique. example, For conotoxin example, MVIIA conotoxin from MVIIAConus magusfrom Conus, was successfully magus, was expressedsuccessfully through expressed a recombinant through a methodologyrecombinant methodology using a thioredoxin using aN thioredoxin-terminal fusion N‐terminal tag, a His-tag fusion tag, for purification,a His‐tag for and purification, a BL21 (DE3) and E.a BL21 coli host (DE3) without E. coli any host cleavage without ofany the cleavage fusion tag of the [120 fusion]. Another tag [120]. conotoxin, Another PrIIIE conotoxin, from Conus PrIIIE parius from, Conus parius, was recombinantly expressed in a similar way, but a small ubiquitin‐like modifier

Toxins 2016, 8, 117 12 of 30 was recombinantly expressed in a similar way, but a small ubiquitin-like modifier (SUMO) was used as an N-terminal fusion tag, Rosetta-gami B (DE3) was used as the E. coli host, and the fusion tag was cleaved using SUMO protease [127]. We recently described a method for the recombinant expression and characterization of terebrid teretoxin peptide Tgu6.1, from Terebra guttata [111]. The teretoxin Tgu6.1 is a novel 44-amino acid teretoxin peptide with a cysteine scaffold similar to the VI/VII framework (C-C-CC-C-C) of the I, M, and O-superfamilies found in cone snails. The recombinant Tgu6.1 was synthesized using a ligation independent cloning strategy with an ompT protease-deficient strain of E. coli. Specific care in plasmid design was taken to combat challenges commonly associated with recombinant expression, such as the formation of insoluble protein aggregates in E. coli, proteolytic degradation, and unfavorable conditions in E. coli cytoplasm that can prevent the formation of disulfide bonds. In the case of Tgu6.1, thioredoxin was introduced in the plasmid for disulfide folding and solubility issues, His6-tag and Ni-NTA (nickel-nitrilotriacetic acid) affinity chromatography were used as a purification method, and enterokinase was applied to site-specifically cleavage Tgu6.1 from the fusion protein. The recombinantly expressed Tgu6.1 peptide exhibited bioactivity, displaying a paralytic effect when tested in a bioassay using the native prey or terebrids, Nereis virens (Annelida) [111]. As the demand for therapeutic peptide drugs increases it is crucial to have reliable methods for obtaining significant amounts of disulfide-rich venom peptides. The recombinant expression technique applied to Tgu6.1 described above is an effective alternative to SPPS of teretoxins and other disulfide-rich venom peptides.

5. Characterization of Teretoxin Structure Determining disulfide connectivity in venom peptides is a fundamental step in establishing structure–function relationships. The disulfide crosslinks in venom peptides provide the structural scaffolds that are essential for their recognition at specific receptor sites [131,132]. An important aspect to determine disulfide connectivity is the ability to sequester fragments containing single disulfide bonds through MS fragmentation [133]. As most venom peptides are highly disulfide-rich, the number of disulfide bond isomers rapidly increases with the number (n) of disulfide bonded Cys residues: the general formula being n!/[(n/2)!2n/2]. Traditionally, the determination of disulfide pairing in proteins/peptides was extremely labor-intensive, applying separation of proteolytic fragments by electrophoresis in one dimension, followed by performic acid oxidation and paper chromatographic separation in the other [134]. A theoretically ideal method to determine disulfide frameworks in venom peptides is X-ray crystallography as the dense sulfur atoms in the cysteine side chains scatter electrons well and are therefore readily visible in electron density maps. Unfortunately, the inherent flexibility and small size of most venom peptides make them difficult to crystallize [133,135]. The most commonly used methods for characterization of disulfide bonds involve selective reduction and alkylation of the peptide at low pH followed by Edman sequencing of a panel of partially reduced intermediates or cleavage of the peptide with proteolytic enzymes followed by isolation and MS/MS analysis of the resulting fragments [136,137].

5.1. Characterization of Teretoxin Disulfide Motif We have recently determined the disulfide connectivity of teretoxin Tv1 from Terebra variegata by MS/MS mapping using a partial reduction and dual alkylation protocol applying TCEP-HCl (Tris(2carboxyethyl)phosphine hydrochloride) as reducing agent and NEM (N-ethylmaleimide) and IAM (iodoacetamide) as alkylating agents. Dual NEM/IAM alkylation resulted in Tv1 peptide species that were labeled with two, four, or six NEM and IAM groups. The location of NEM and IAM modifications in each of the six partially reduced species was determined by matching the MS/MS b- and y-series ions to theoretical patterns [72]. The solution structure of Tv1 was independently derived using standard homonuclear proton NMR techniques on unlabeled folded synthetic peptide to confirm the disulfide bond connectivity Toxins 2016, 8, 117 13 of 30 derived from MS/MS (Figure5C). Proton assignments were obtained from 2D NOESY and TOCSY spectra, and carbon chemical shifts were assigned with the help of a natural-abundance 13C-HSQC spectrum. Disulfide connectivities were then determined based on the proximity of cysteine residues in the 10 lowest-energy structures and were in agreement with the disulfide bond pattern derived by MS/MS analysis [72]. Teretoxin Tv1 has a unique fold compared to other venom peptides. The Cys7 to Cys16 β-hairpin is clamped together, and the N- and C-terminal loops are clamped through the Cys4-Cys20 and Cys5-Cys21 double-disulfide bond arrangement in an antiparallel manner that flattens the peptide into an ellipsoid shape (Figure5C).

5.2. In Silico Peptide Structure Determination With the large numbers of putative venom peptides identified recently through NGS approaches, it is prohibitively time-consuming and expensive to structurally characterize each of these peptides using NMR, especially considering that some of the identified peptides might be false positives that represent artifacts of the NGS assembly methods. Bioinformatics algorithms that predict the three-dimensional structures of peptides can be used to narrow down which of many candidate peptide toxins are worthy of experimental characterization [138] (Figure6). Venom peptide sequences that fold into three-dimensional structures with high confidence are more likely choices for structural and experimental characterization than those that do not form stable folds or display many conformations with no clear global minimum. Using this in silico approach, the number of peptides that are synthesized and characterized could be significantly reduced to a manageable amount.

Figure 6. Predicting 3D structure of venom peptides. Scatter plot representation of Rosetta scores for each of the 10,000 attempts to fold α-GID conotoxin from its amino acid sequence. Blue circles represent each folding attempt and the red circle represents a folding simulation that resulted in the correct structure. Inset: comparison of α-GID NMR structure (green) and Rosetta structure prediction (red). Rosetta ab initio folding protocol was used to predict structure and scores were calculated as the Root-Mean-Square Deviation (RMSD) to the NMR structure of α-GID.

The Rosetta algorithm for protein folding has enjoyed considerable success in accurately predicting the three-dimensional structure of proteins ab initio from their sequence, including the prediction of a completely new protein fold [138,139]. Rosetta is well suited for the folding of venom peptides from their primary sequence as the disulfide connectivity of these peptides significantly reduces the number of conformations that need to be searched. Even with this constraint, there is Toxins 2016, 8, 117 14 of 30 evidence that a very large amount of sampling will be necessary for accurate structure prediction, as venom peptide conformations are unusual in that they differ from the typical, globular conformations of most proteins [140]. Compared to simpler sequence based approaches that neglect to consider information about the three-dimensional structure of venom peptides, Rosetta can be used as a more robust filter for screening the more than one million estimated conoidean venom peptides identified using venomics.

6. Teretoxins Bioactivity Assays and Functionalization Our integrative venomics strategy follows a funneling approach from organismal to molecular biology, starting with the description of terebrid venomous lineages and the characterization of teretoxin peptides, and ending with the identiﬁcation of speciﬁc molecular targets and functions (Figure1). In the sections below we describe our particular methodology to investigate teretoxin bioactivity and molecular targets.

6.1. Biological Assays To determine the biological activity of selected synthesized teretoxins, peptides are initially tested on a bioassay using their native prey, polychaete worms (Annelida) (Figure1). Animal assays have proven very useful to gain initial phenotypic insight in to the function of conoidean peptides [141]. Teretoxin polychaete assays are conducted by injecting the folded terebrid peptide in the ventral nerve cord of a polychaete. Two additional polychaetes are usually injected with saline solution as a negative control, and a well-characterized peptide toxin (e.g., agatoxin) as a positive control [39,72,111]. Two recently characterized teretoxins, Tv1 and Tgu6.1, analyzed using this bioassay caused partial paralysis in Nereis virens polychaetes [72,111]. As Terebridae native prey, polychaete worms are the first line of attack to determine bioactivity in terebrid venom peptides. More complex animal assays, such as rat or mouse models, are the next step and routinely used to assay venom activity [142–147]. While functionality and activity in native prey are not directly tied to drug discovery, conducting native prey assays ensures that the newly identified peptide is synthesized and folded correctly. It is important to verify that the peptide scaffold being applied for drug development is an accurate scaffold. Additionally, screening for venom peptide molecular targets and bioassays for potential biomedical applications is very labor-intensive, so focusing on peptides that show bioactivity in native prey narrows the pool of candidates to those that have greater potential. Finally, the phenotypic response in the native prey can help identify the molecular mechanism of the venom peptide, e.g., if it shows paralytic effect or hyperactivity this may suggest a possible molecular ion channel target based on previous peptides screened. More recently, due to the increasing interest in venom peptides as candidates for drug discovery, microfluidic techniques using cell cultures are also being applied to assay crude venom extracts and purified peptide toxins [148,149]. One of the main advantages of microfluidics is that it allows for fast high-throughput screening of venom peptides to rapidly identify bioactive compounds and their potential molecular targets.

6.2. Characterizing Molecular Function Venom peptides typically interact with ion channels and modulate their activities, enabling the investigation of specific ion channels and their function [150,151]. For that purpose, after bioactivity is confirmed through a phenotypic screen, the next step is to determine the molecular target of the peptide toxin. Characterizing the molecular activity of venom peptides is important from a basic scientific perspective, but also critical from a therapeutic point of view, as knowing the mechanism of action of a molecule is a prerequisite for moving it through clinical trials. Additionally, identifying the molecular site of action of venom peptides enables an ensemble of structure-based molecular design methods to optimize the peptides use as effective drugs. However, identifying the receptors on which a venom peptide is active can be as difficult as finding a needle in a haystack. Toxins 2016, 8, 117 15 of 30

Virtual screening is a well-established computational method for identifying ligands that interact with target proteins. It has been applied successfully even to challenging problems such as finding small molecules that are active at a target protein whose structure is not known, such as a G-Protein Coupled Receptors (GPCRs) [152]. In theory, virtual screening methods could also be used to identify the molecular targets of a newly discovered venom peptides. However, in practice this could be challenging due to the laborious nature of constructing individual models of venom peptides with a variety of different potential molecular targets—such as nicotinic receptors, voltage-gated sodium and calcium channels, and Transient Receptor Potential (TRP) channels—and those for which there is no solved NMR or crystal structure of the peptide or molecular receptor. Molecular modeling environments that integrate bioinformatics, homology modeling, and docking algorithms can drastically reduce the time needed to create in silico venom peptide models. In this regard, virtual screening can be used in conjunction with high-throughput bioassay screening methods to prioritize which molecular targets to screen against. For example, the Bioluminate software package (Schrodinger; New York, NY, USA) largely automates all the steps in the homology model process, including special features that allow more sensitive searches for distant structural homologues of ion channels to use as templates. The entire homology modeling process takes only a few minutes. The venom peptide can then be docked against the model using the integrated PIPER protein–protein docking algorithm. The entire process, including simulation time, takes roughly one hour for a given ion channel or receptor target. While the results of such an in silico screen may not always be entirely accurate, they can be improved by including mutagenesis constraints from the literature if available. Such an effort can thus provide a prioritized list of molecular targets for screening (i.e., start with sodium channels prior to calcium channels), potentially reducing the time and material necessary to identify the molecular channels targeted by venom peptides. Our approach to teretoxin molecular target discovery is to apply computational algorithms to model the docking of the peptide of interest to a wide range of potential receptors. The docking poses can be refined with long timescale Molecular Dynamics (MD) simulations of the peptide toxin/receptor pose. If the peptide remains in a well-defined pose over the timescale of hundreds of nanoseconds or several microseconds, it suggests that the teretoxin effectively binds the target receptor protein. These receptors are then selected as the more likely candidates and have the highest priority for further experimental verification. Alternatively, receptors where the peptide never establishes a well-defined pose are considered less likely to be the true target of the peptide and are discarded for experimental testing.

7. Optimization of Venom Peptides for Drug Development The estimate of available venom peptides from the reservoir of conoidean snails alone is upwards of one million compounds [19]. Giving this enormous grab bag, it is essential to identify methods for optimizing the selection of venom peptides for drug development. Prialt®, Byetta®, and Captopril® are all breakthrough drugs derived from animal venom peptides via different routes, decades after their initial discoveries [6–8]. However, with the promise of venomics, peptides that lead to therapeutics can be more effectively identiﬁed in a strategic manner. It should be noted that venom peptides, while more stable due to their disulﬁde-rich content, are still susceptible to hurdles that prevent their widespread application as therapeutics, namely poor pharmacokinetics and invasive delivery methods [153,154]. The sections below outline the strategies that we apply to optimize the potential biomedical applications of teretoxins.

7.1. Computational Design for Increased Affinity and Selectivity of Peptide Toxins Native venom peptides have remarkable affinity and specificity for drug targets such as ion channels and GPCRs. Many venom peptides can readily serve as scaffolds for peptidomimetics or pharmacological research tools; however, with a few exceptions, most venom peptides often require derivative versions to be useful as therapeutic leads. A common modification applied to venom Toxins 2016, 8, 117 16 of 30 peptides is devising derivatives to increase affinity for a specific molecular receptor [155,156]. Another modification often required for peptide toxins is cyclization to increase the potential for oral activity Toxins 2016, 8, 117 16 of 29 and longevity for in vivo circulation [157–160]. AnotherTraditional modification methods often for required identifying for peptide specific toxins functional is cyclization mutations to increase in venom the potential peptides for oral include trialactivity and error and longevity alanine for walks in vivo through circulation each [157–160]. residue of the peptide. Following this approach, 20 potentialTraditional functional methods mutations for identifying in conotoxin specific αfunctional-GID and mutations 70 mutants in venom of spider peptides peptide include GpTx-1 weretrial identified and error [161 alanine,162]. walks However, through there each is residue no guarantee of the peptide. that any Following of these this alanine approach, mutants 20 will havepotential the desired functional pharmacological mutations in profileconotoxin [163 α‐].GID Moreover, and 70 mutants alanine of scans spider followed peptide GpTx by synthesis‐1 were and characterizationidentified [161,162]. of each However, mutant isthere costly is no and guarantee time-consuming. that any of these A modern alanine alternative mutants will to have alanine the scans desired pharmacological profile [163]. Moreover, alanine scans followed by synthesis and involves bioinformatics algorithms in which different mutations to the peptide toxin can be applied characterization of each mutant is costly and time‐consuming. A modern alternative to alanine scans in silico and their effects on affinity and selectivity of binding to specific receptors can be predicted involves bioinformatics algorithms in which different mutations to the peptide toxin can be applied computationallyin silico and their [164 effects,165]. on The affinityin silico andmethod selectivity is bothof binding inexpensive to specific and receptors rapid comparedcan be predicted to alanine scans,computationally ensuring that [164,165]. the number The ofin venomsilico method peptides is both examined inexpensive can beand significantly rapid compared increased. to alanine scans,Rosetta ensuring is one ofthat the the most number widely of venom used and peptides successful examined algorithms can be significantly for in silico molecularincreased. design [139]. In additionRosetta to being is one used of the for most modeling widely theused 3D and structure successful of algorithms proteins, asfor discussed in silico molecular previously, design Rosetta can also[139]. be In applied addition to to model being andused design for modeling peptide/receptor the 3D structure complexes, of proteins, including as discussed modules previously, for structural refinement,Rosetta can protein–peptide also be applied docking, to model and and proteindesign peptide/receptor design [166–168 complexes,]. Additionally, including Rosetta modules has for recently beenstructural extended refinement, to incorporate protein–peptide non-canonical docking, amino and protein acids, design therefore [166–168]. it can Additionally, also model Rosetta and design has recently been extended to incorporate non‐canonical amino acids, therefore it can also model and post-translational modifications such as, hydroxylation, sulfation, and others commonly found in design post‐translational modifications such as, hydroxylation, sulfation, and others commonly venom peptides [105,169]. We are currently using Rosetta to increase conotoxin and teretoxin selectivity found in venom peptides [105,169]. We are currently using Rosetta to increase conotoxin and for specificteretoxin molecular selectivity targets for specific (Figure molecular7). As targets part of (Figure this effort, 7). As we part are of developing this effort, we an are application developing inside the Rosettaan application framework inside to the more Rosetta accurately framework predict to peptidemore accurately toxin affinity predict and peptide specificity toxin byaffinity incorporating and the flexibilityspecificity ofby theincorporating peptide/receptor the flexibility complex of the intopeptide/receptor the scoring complex calculation. into the When scoring complete, calculation. this tool will beWhen made complete, publicly this available tool will be via made Rosetta’s publicly webserver available ROSIEvia Rosetta’s [170]. webserver ROSIE [170].

A B

Figure 7. Structure‐guided design of venom peptides. (A) Structure of acetylcholine binding protein Figure(AchBP) 7. Structure-guided in complex with conotoxin design α‐ ofPnIA. venom AchBP peptides. subunits ((greenA) Structure and purple) of acetylcholinehave a pentameric binding proteinarrangement (AchBP) around in complex a central with pore. conotoxin Conotoxinα α‐-PnIA.PnIA AchBP(red) binds subunits at the (greeninterface and of consecutive purple) have a pentamericsubunits. arrangement (B) Atomic interactions around a between central α‐ pore.PnIA Conotoxinat the interfaceα-PnIA of AchBP (red) subunits. binds atHydrophobic the interface of consecutiveinteractions subunits. (green) are (B )highly Atomic prevalent, interactions but positive between and αnegative-PnIA interactions at the interface are also of present. AchBP The subunits. HydrophobicAchBP binding interactions pocket is (green) extensively are exposed highly prevalent, to solvent (gray but positive clouds) complicating and negative the interactions computational are also present.modeling. The AchBP binding pocket is extensively exposed to solvent (gray clouds) complicating the computational modeling. 7.2. Identification of Key Residues in Venom Peptides

Toxins 2016, 8, 117 17 of 30

7.2. Identification of Key Residues in Venom Peptides Rosetta on its own will identify potential residues that can be altered to enhance venom peptide specificity. However, if we provide Rosetta with the information accumulated through millions of year of evolution inherent to venom peptide genetic sequences, we can significantly boost its efficiency. Computational algorithms that estimate sequence evolution such as PAML (Phylogenetic Analysis by Maximum Likelihood) and HyPhy (Hypothesis testing using Phylogenies), can compute the rate of non-synonymous to synonymous mutations in a given group of sequences, identifying specific sites of the venom peptides under positive selection [171–173]. As these sites are not evolutionarily conserved, with diverse amino acids present in different bioactive peptides, they represent excellent targets to mutate in silico with Rosetta. By combining Rosetta modeling with evolutionary algorithms, we can optimize the process of identifying random mutation possibilities, focusing only on those that have passed the test of millions of years of evolutionary change while maintaining venom peptide bioactivity. In venom research, evolutionary algorithms have been primarily used to answer questions about venom peptide evolution [174–178]. Venoms are generally under strong positive selection to counteract the evolving defenses of their prey in a never-ending predator-prey arms race [179–181]. Although traditionally used to investigate evolutionary patterns, evolutionary algorithms can also be effectively applied to predict which amino acids can be altered to increase the affinity and selectivity of a venom peptide to its target [182–184]. For example, PAML was successfully used to identify four positively selected sites in scorpion α-neurotoxins LqhαIT, Lqh2, Amm8rgp-3, Ac1, Ac4, Lqh3.1, and Bjα2 that target voltage-gated sodium channels [182]. Two of the four sites identified by PAML as being positively selected had been previously linked to bioactivity in peptides LqhαIT and Lqh2. Additionally, after mutagenesis analysis of these positions, the peptides displayed enhanced potency and selectivity for sodium channels [182–184]. Conversely, another study used similar methods to investigate evolutionary patterns in scorpion α-neurotoxin receptors, namely the sodium channels of the scorpion’s prey, and discovered that scorpion venom peptides bind to evolutionarily variable regions of the sodium channels [185]. Specifically, positively selected sites of scorpion α-neurotoxins bind to sodium channels sites under relaxed purifying selection [185]. These findings highlight how venom peptides interact with their molecular targets and indicate specific sites of the peptide and receptor that could potentially be altered to increase selectivity. Therefore, information derived from evolutionary algorithms such as PAML and HyPhy can be coupled with Rosetta software to effectively enhance its predictive properties and increase venom peptide and receptor specificity.

8. Venom Peptide Drug Delivery The potency and specificity of bioactive peptides have propelled these agents to the forefront of pharmacological research, but delivery of peptides to their molecular target is a major obstacle to their widespread application. We have recently devised a Trojan Horse strategy consisting of packaging a bioactive peptide within a modified protein cage to protect it during transport, and releasing it at the target site, which has proven to be a very promising delivery method [186] (Figure8). As mentioned earlier, a major obstacle to the medical application of molluscan venom peptides, and indeed peptides in general, is their poor pharmacokinetic profile. In addition, peptides generally exhibit poor membrane solubility and can be rapidly cleared through the liver and kidneys [154]. Finally, the blood–brain barrier (BBB) prevents neuroactive peptides in the bloodstream from reaching targets in the central nervous system (CNS), resulting in these compounds being administered through intrathecal injection [187]. General methods for improving the pharmacokinetic profile of bioactive peptides are necessary if these compounds are to realize their full therapeutic potential. There are numerous strategies for improving the pharmacokinetic profile of therapeutic peptides. One approach is to stabilize the structure of the peptide itself through such methods as peptide stapling, macrocyclization, or grafting of peptide segments onto a small protein scaffold [157–160,188]. However, as these methods involve changes in secondary and tertiary structure, they can disturb the Toxins 2016, 8, 117 18 of 30 Toxins 2016, 8, 117 18 of 29 canfunction disturb and the bioactivity function and of the bioactivity peptide. of In the addition, peptide. these In addition, methods these are methods not completely are not general completely and generalmust be and specifically must be adaptedspecifically to each adapted individual to each peptide. individual peptide.

FigureFigure 8. 8. TrojanTrojan Horse Horse teretoxin teretoxin delivery delivery strategy. strategy. Schematic Schematic overview overview of peptide of peptide drug drug delivery delivery via virusvia virus-like‐like particle particle (VLP) (VLP) nanocontainers. nanocontainers. The peptide The peptide cargo cargois first is encapsulated first encapsulated in the inVLP the using VLP recombinantusing recombinant biology. biology. The VLP The exterior VLP exterior is modified is modified with the with cell the‐penetrating cell-penetrating peptide peptide HIV‐Tat HIV-Tat and norborneneand norbornene to enable to enable transport transport to target to target site site and and disassembly disassembly respectively. respectively. The The modified modified VLP nanocontainernanocontainer is is transported transported to to the the target target site, site, disassembly disassembly is is triggered triggered by by Grubbs Grubbs II II catalyst catalyst and and the the peptide cargocargo is is released. released. The The modular modular strategy strategy outlined outlined allows forallows substitution for substitution of alternate of conjugates, alternate conjugates,cargo proteins, cargo and proteins, disassembly and disassembly mechanisms. mechanisms.

As an alternative to modifying the peptide, our Trojan Horse strategy involves packaging the As an alternative to modifying the peptide, our Trojan Horse strategy involves packaging peptide of interest within a macromolecular nanoparticle that can deliver it to its molecular target the peptide of interest within a macromolecular nanoparticle that can deliver it to its molecular and protect it from degradation during transport. Several types of macromolecules have been target and protect it from degradation during transport. Several types of macromolecules have investigated as potential drug‐delivery nanocontainers including liposomes [189], natural and been investigated as potential drug-delivery nanocontainers including liposomes [189], natural and synthetic polymers [190], inorganic particles [191,192], DNA origami structures [193], and protein synthetic polymers [190], inorganic particles [191,192], DNA origami structures [193], and protein cages such as ferritins and virus‐like particles (VLPs) [194,195]. Nanoparticle delivery systems are cages such as ferritins and virus-like particles (VLPs) [194,195]. Nanoparticle delivery systems are essentially modular, because their packaging, delivery, and targeting properties are determined by essentially modular, because their packaging, delivery, and targeting properties are determined by the the nanoparticle carrier rather than by the therapeutic compound. As a result, a single delivery nanoparticle carrier rather than by the therapeutic compound. As a result, a single delivery system system could be used for the delivery of a diverse array of bioactive venom peptides. could be used for the delivery of a diverse array of bioactive venom peptides.

8.1.8.1. P22 P22 Nanocontainers Nanocontainers for for Venom Venom Peptide Drug Delivery OurOur recently developed peptide peptide drug drug delivery method method repurposes repurposes the the procapsid procapsid from from the Salmonella typhimurium bacteriophagebacteriophage P22 P22 as as a nanocontainer for the delivery of ziconotide (Prialt ®,, MVIIA)MVIIA) across across the the blood blood-brain‐brain barrier barrier (BBB) (BBB) [186]. [186]. Similar Similar to to other other viral viral capsids, capsids, the the P22 P22 procapsid procapsid is wellis well-deﬁned,‐defined, monodisperse, monodisperse, easy easy to tomanufacture, manufacture, and and amenable amenable to to both both chemical chemical and and genetic manipulationmanipulation [196]. [196]. By By modifying modifying the the scaffold scaffold protein protein that that templates templates the the self self-assembly‐assembly of of the the P22 procapsid, anan arbitrary arbitrary gene gene product product can can be incorporatedbe incorporated within within the procapsid the procapsid shell [197 shell,198 [197,198].]. Among Amongthe proteins the proteins that have that been have successfully been successfully packaged packaged within the within procapsid the procapsid are the ﬂuorescent are the fluorescent proteins proteinsEGFP, mCherry, EGFP, mCherry, and ziconotide and ziconotide [186,197,199 [186,197,199].]. VLPs have a number of significant advantages compared with other macromolecules: First, they are generally uniform in size and composition and possess defined architectures—traits that can

Toxins 2016, 8, 117 19 of 30

VLPs have a number of significant advantages compared with other macromolecules: First, they are generally uniform in size and composition and possess defined architectures—traits that can allow for precise control of pharmacological properties. Second, as proteins, they are biodegradable by endogenous cellular pathways, reducing the ability to accumulate in an organ. Also, as gene products, VLPs can be produced relatively easily and in high yields using standard molecular biology protocols. Finally, a plethora of tried-and-tested protein modification techniques are available for manipulating the interior and exterior of proteinaceous VLPs such as molecular cloning, standard and unnatural amino acid mutagenesis, protein bioconjugation, and directed evolution [200]. In general, the adaptation of protein cages for drug delivery involves three distinct steps: (1) encapsulation of the pharmacological agent within the viral capsid; (2) targeting of the capsid to the desired site in vivo; and (3) induced disassembly of the capsid and release of the cargo under physiological conditions (i.e., neutral pH, moderate temperature, and aqueous environment) (Figure8). Applying our Trojan Horse strategy, we have successfully transported P22 VLPs loaded with the conotoxin-derived analgesic ziconotide (Prialt®), across in vitro and in vivo BBB models. Briefly, the cell-penetrating HIV-Tat peptide (YGRKKRRQRRR) was synthesized, fluorescently labeled, and activated with maleimidopropionic acid (MPA), then conjugated to a P22 nanocontainer preloaded with ziconotide and engineered to feature a surface exposed cysteine residue. P22-Tat nanocontainers translocated the BBB, demonstrating the feasibility of this Trojan Horse strategy [186]. At a size of ~54 nm in diameter, P22 capsid virus-like particles are significantly larger than the proteins and quantum dots previously translocated and reported in the literature. This was the first demonstration of delivery of ziconotide across a BBB model using a nanoparticle delivery system, providing an alternative route to intrathecal injection, which has thus far been the only delivery method. The results of this proof-of-concept experiment are promising towards the development of a tunable VLP nanocontainer for the delivery of peptide therapeutics across the BBB [186,199].

8.2. Release of Venom Peptide at Molecular Target Site The next step in the adaptation of VLPs for drug delivery, namely controllable disassembly, remains a challenge, mainly due to the fact that the disassembly mechanism must proceed under physiological aqueous environment, with moderate temperature and neutral pH. Overcoming this obstacle requires an integrated approach at the intersection of chemistry and biology, with a particular emphasis on materials science, structural virology, and protein engineering. Recent advances in bioorthogonal chemistry have led to the identification of numerous reactions that proceed under physiological conditions. We are currently investigating the Ring Opening Metathesis Polymerization (ROMP) triggered by a ruthenium catalyst (Grubbs II catalyst) for the controlled disassembly of the P22 VLP (Figure8). ROMP is a polymerization reaction initiated by a transition-metal catalyst and driven by the release of ring strain in a cyclic olefin such as cyclobutene, cyclopentene, cis-cyclooctene, or norbornene [201]. The ROMP disassembly strategy aims to disrupt the capsid architecture through steric strain brought about by the unfolding polymerization reaction [202–204]. While most ROMP catalysts, including all of the commercially available catalysts, use ruthenium, ROMP with molybdenum and tungsten catalysts have also been reported [205]. As we have previously demonstrated, norbornene is readily attached to the surface of the P22 procapsid through traditional bioconjugation techniques [186,199]. We have recently found that conjugation of P22-GFP procapsids with NHS-activated 5-norbornene-2-carboxylic acid yields P22-GFP-Norb procapsids with an average of 4.12 norbornenes per coat protein monomer, or more than 1700 norbornenes per procapsid [199]. While the coat protein monomer contains 19 lysine residues, not all of these are surface exposed. TEM analysis revealed that conjugation of norbornene to the capsid surface did not significantly affect size or morphology. However, treatment of P22-GFP-Norb with Grubbs II catalyst, which initiates ROMP, produced clusters of P22-GFP-Norb with distorted morphologies that appeared to be joined by robust bridge structures, suggesting that ROMP occurred at both intra- and inter-nanocontainer Toxins 2016, 8, 117 20 of 30 interfaces [199]. Further investigation remains to be done to characterize the triggered disassembly of P22 VLPs, but these early results suggest that our Trojan Horse strategy is an effective method for the delivery of peptide therapeutics, including teretoxins.

9. Conclusions This review describes our learn-from-nature integrative venomics strategy for the discovery and characterization of terebrid venom peptides (Figure1). This multidisciplinary strategy from mollusks to medicine starts with the phylogenetic delimitation of venomous Terebridae lineages and putative teretoxins, continues with the chemical and recombinant synthesis of promising peptide toxins and their structural characterization, followed by assays to determine bioactivity and molecular targets, and concludes with the optimization of venom peptides as drug leads and the development of effective strategies for delivery of venom peptide therapeutics. While a signiﬁcant amount of research remains to be done, it is clear that venoms are nature’s cocktail for drug discovery and an integrated venomics strategy is a successful route to identifying the most effective ingredients to develop potent and selective peptide therapeutics.

Acknowledgments: The authors thank all current and past members of the Holford group, especially the undergraduate students who contributed to the research highlighted in this review: Peter Filipenko, Marouf Hossain, Yasmine Karma, Manjeet Kaur, Samer Khawaja, Emily Lau, Michael Lyudmer, Sujoy Manir, John Moon, Elena Pires, Carolina Santamaria, Chhime Sherpa, Hye Young Shin, Nicolette Somogyi, Alex Uvaydov, Laurel Yee, Henry Yelkin, and Michelle Yun. Mandë Holford acknowledges funding from the Camille and Henry Dreyfus Teacher-Scholar Award, NSF awards CHE-1247550 and CHE-1228921, NIH-NIMHD grant MD007599, PSC-CUNY Enhanced Collaborative Grant CIRG2064, Weill Cornell CTSC Award 5 UL1 TR000457-09, and Alfred P. Sloan CUNY Junior Faculty Award. Juliette Gorson, Stephen Jannetti, Patrick Kelly, and Aida Verdes acknowledge the support provided by the Graduate Center of the City University of New York Science Scholarship. Aida Verdes acknowledges additional support from the Natural Sciences Department of Baruch College. Abba Leffler acknowledges support from an NIH T32 Training Grant 3T32MH096331-04S1. Author Contributions: M.H. and A.V. conceived and wrote the manuscript and created figures; P.A., J.G., S.J., P.K., A.L., D.S., and G.R. contributed to specific sections and created figures. All authors read and approved the manuscript. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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